Representativeness and Face-Ism: Gender Bias in Image Search

Ulloa, Roberto; Richter, Ana Carolina; Makhortykh, Mykola; Urman, Aleksandra; Kacperski, Celina Sylwia (28 May 2022). Representativeness and Face-Ism: Gender Bias in Image Search (Unpublished). In: 72nd Annual ICA Conference - "One world, one network?!". Paris, France. 26.05.-30.05.2022.

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Implicit and explicit gender biases in media representations of individuals have long existed. Women are less likely to be represented in gender-neutral media content (representational bias), and their face-to-body ratio in images is often lower (face-ism bias). In this paper, we look at representativeness and face-ism in search engine image results. We systematically queried four search engines (Google, Bing, Baidu, Yandex) from three regions, using two browsers and in two waves, with gender-neutral (person, intelligent person) and gendered (woman, intelligent woman, man, intelligent man) terminology, accessing the top 100 image results. We employed automatic identification for the individual’s gender expression (female/male) and calculation of the face-to-body ratio of individuals depicted. We find that, as in other forms of media, search engine images perpetuate biases to the detriment of women, confirming the existence of the representational and face-ism biases. In-depth algorithmic debiasing with a specific focus on gender bias is overdue.

Item Type:	Conference or Workshop Item (Paper)
Division/Institute:	03 Faculty of Business, Economics and Social Sciences > Social Sciences > Institute of Communication and Media Studies (ICMB)
UniBE Contributor:	Makhortykh, Mykola, Urman, Aleksandra
Subjects:	000 Computer science, knowledge & systems 300 Social sciences, sociology & anthropology 300 Social sciences, sociology & anthropology > 360 Social problems & social services
Language:	English
Submitter:	Mykola Makhortykh
Date Deposited:	23 Jun 2022 14:59
Last Modified:	05 Dec 2022 16:20
Uncontrolled Keywords:	gender, bias, web search, search engines, algorithms, algorithm audit
URI:	https://boris.unibe.ch/id/eprint/170659